Drivocopter: A concept Hybrid Aerial/Ground vehicle for long-endurance mobility

Kalantari, Arash; Touma, Thomas; Kim, Leon; Jitosho, Rianna; Strickland, Kyle C.; Lopez, Brett T.; Agha–mohammadi, Ali–akbar

doi:10.1109/aero47225.2020.9172782

Cited by 26 publications

(16 citation statements)

References 13 publications

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“…NeBula relies on multiple simulator configurations at different fidelity levels to enable faster and more focused development. Examples include a high-fidelity Gazebo simulator for testing ground vehicle behaviors, flight stack software-in-the-loop simulations for analyzing flight performance, a docker-based multi-robot Autonomous Rollocopter (Fan et al, 2019;Lew et al, 2019a), DrivoCopter (Kalantari et al, 2020), Baxter (Choi et al, 2020), Spherical Rollocopter (Sabet et al, 2020;Sabet et al, 2019).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Passive-wheeledrollocopter (PW-rollocopter)(Fan et al, 2019;Lew et al, 2019a) uses two independent passive wheels to enable large sensory FOV and autonomy. To reduce propeller-generated dust, Drivocopter(Kalantari et al, 2020) and BAXTER(Choi et al, 2020) implement dedicated wheeled actuators, at the expense of a slight reduction in flight time. The latest work is focused on omnidirectional spherical Rollocopters that provide maximum agility and maneuverability while having the minimum friction(Sabet et al, 2020;Sabet et al, 2019).…”

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confidence: 99%

“…Figure 49: Hybrid platform hardware evolution (top row: left to right) pre-challenge Hytaq design (Kalantari and Spenko, 2014), Modular hybrid vehicle (Agha-mohammadi et al, 2020), (bottom row: left to right) Autonomous Rollocopter(Fan et al, 2019;Lew et al, 2019a), DrivoCopter(Kalantari et al, 2020), Baxter(Choi et al, 2020), Spherical Rollocopter(Sabet et al, 2020;Sabet et al, 2019).…”

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confidence: 99%

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NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

Agha–mohammadi¹,

Otsu²,

Morrell³

et al. 2021

Preprint

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This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.

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Section: Simulation Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

Agha–mohammadi¹,

Otsu²,

Morrell³

et al. 2021

Preprint

Self Cite

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“…-Separate Articulation in Terrestrial Operation: Platforms utilizing separate means of propulsion in terrestrial mode are classified to have Active Terrestrial Mode of operation. Vehicles with Active Terrestrial Mode [6,7], compared to vehicles with Passive Terrestrial Mode [2,4,5], provide a more efficient and mutually independent operation, while their payload capacity is partially reduced due to the addition of the driving system. -Morphing Mechanism: Platforms utilizing separate built-in mechanisms for changing the internal form factor are Morphing platforms.…”

Section: Related Workmentioning

confidence: 99%

BAXTER: Bi-modal Aerial-Terrestrial Hybrid Vehicle for Long-endurance Versatile Mobility: Preprint Version

Choi¹,

Wee²,

Corah³

et al. 2021

Preprint

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Unmanned aerial vehicles are rapidly evolving within the field of robotics. However, their performance is often limited by payload capacity, operational time, and robustness to impact and collision. These limitations of aerial vehicles become more acute for missions in challenging environments such as subterranean structures which may require extended autonomous operation in confined spaces. While software solutions for aerial robots are developing rapidly, improvements to hardware are critical to applying advanced planners and algorithms in large and dangerous environments where the short range and high susceptibility to collisions of most modern aerial robots make applications in realistic subterranean missions infeasible. To provide such hardware capabilities, one needs to design and implement a hardware solution that takes into the account the Size, Weight, and Power (SWaP) constraints. This work focuses on providing a robust and versatile hybrid platform that improves payload capacity, operation time, endurance, and versatility. The Bi-modal Aerial and Terrestrial hybrid vehicle (BAXTER) is a solution that provides two modes of operation, aerial and terrestrial. BAXTER employs two novel hardware mechanisms: the M-Suspension and the Decoupled Transmission which together provide resilience during landing and crashes and efficient terrestrial operation. Extensive flight tests were conducted to characterize the vehicle's capabilities, including robustness and endurance. Additionally, we propose Agile Mode Transfer (AMT), a transition from aerial to terrestrial operation that seeks to minimize impulses during impact to the ground which is a quick and simple transition process that exploits BAXTER's resilience to impact.

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“…Many drones are fitted with active and/or passive wheels that can be used when flying is not possible. For instance, [9] and [10] classify and explain different configuration of legged and wheeled aerial systems along with their advantages and downfalls. The study also proposes a multi-modal aerial vehicle called DrivoCopter, featuring active meshed spherical wheels that surround the propellers and protect the propellers during collision.…”

Section: Introductionmentioning

confidence: 99%

A Letter on Progress Made on Husky Carbon: A Legged-Aerial, Multi-modal Platform

Adarsh¹,

Manjikian²,

Wang³

et al. 2022

Preprint

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Animals, such as birds, widely use multi-modal locomotion by combining legged and aerial mobility with dominant inertial effects. The robotic biomimicry of this multimodal locomotion feat can yield ultra-flexible systems in terms of their ability to negotiate their task spaces. The main objective of this paper is to discuss the challenges in achieving multimodal locomotion, and to report our progress in developing our quadrupedal robot capable of multi-modal locomotion (legged and aerial locomotion), the Husky Carbon. We report the mechanical and electrical components utilized in our robot, in addition to the simulation and experimentation done to achieve our goal in developing a versatile multi-modal robotic platform.

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Drivocopter: A concept Hybrid Aerial/Ground vehicle for long-endurance mobility

Cited by 26 publications

References 13 publications

NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

BAXTER: Bi-modal Aerial-Terrestrial Hybrid Vehicle for Long-endurance Versatile Mobility: Preprint Version

A Letter on Progress Made on Husky Carbon: A Legged-Aerial, Multi-modal Platform

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